
  !pip install xgboost

Requirement already satisfied: xgboost in /usr/local/lib/python3.10/dist-packages (2.0.3)
  Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from xgboost) (1.25.2)
  Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from xgboost) (1.11.4)


  #import libraries
  import math
  import pandas as pd
  import numpy as np
  from sklearn import metrics
  import matplotlib.pyplot as plt
  from matplotlib.gridspec import GridSpec
  %matplotlib inline
  import warnings
  warnings.filterwarnings('ignore')
  import seaborn as sns
  from sklearn.model_selection import train_test_split, GridSearchCV
  from sklearn.linear_model import LogisticRegression
  from sklearn import metrics
  from sklearn.ensemble import BaggingClassifier, RandomForestRegressor, RandomForestClassifier,GradientBoostingClassifier,GradientBoostingRegressor, AdaBoostClassifier,AdaBoostRegressor, StackingRegressor
  from sklearn.tree import DecisionTreeRegressor,DecisionTreeClassifier
  from xgboost import XGBRegressor,XGBClassifier
  from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error


  #Function to show %
  def autopct_format(values):
          def my_format(pct):
              total = sum(values)
              val = int(round(pct*total/100.0))
              return '{:.1f}%\n({v:d})'.format(pct, v=val)
          return my_format

  # function to create labeled barplots

  def labeled_barplot(data, feature, perc=False, n=None):
      """
      Barplot with percentage at the top

      data: dataframe
      feature: dataframe column
      perc: whether to display percentages instead of count (default is False)
      n: displays the top n category levels (default is None, i.e., display all levels)
      """

      total = len(data[feature])  # length of the column
      count = data[feature].nunique()
      if n is None:
          plt.figure(figsize=(count + 2, 6))
      else:
          plt.figure(figsize=(n + 2, 6))

      plt.xticks(rotation=90, fontsize=15)
      ax = sns.countplot(
          data=data,
          x=feature,
          palette="Paired",
          order=data[feature].value_counts().index[:n],
      )

      for p in ax.patches:
          if perc == True:
              label = "{:.1f}%".format(
                  100 * p.get_height() / total
              )  # percentage of each class of the category
          else:
              label = p.get_height()  # count of each level of the category

          x = p.get_x() + p.get_width() / 2  # width of the plot
          y = p.get_height()  # height of the plot

          ax.annotate(
              label,
              (x, y),
              ha="center",
              va="center",
              size=12,
              xytext=(0, 5),
              textcoords="offset points",
          )  # annotate the percentage

      plt.show()  # show the plot


  def histogram_boxplot(data, feature, figsize=(15, 10), kde=False, bins=None):
      """
      Boxplot and histogram combined

      data: dataframe
      feature: dataframe column
      figsize: size of figure (default (15,10))
      kde: whether to show the density curve (default False)
      bins: number of bins for histogram (default None)
      """
      f2, (ax_box2, ax_hist2) = plt.subplots(
          nrows=2,  # Number of rows of the subplot grid= 2
          sharex=True,  # x-axis will be shared among all subplots
          gridspec_kw={"height_ratios": (0.25, 0.75)},
          figsize=figsize,
      )  # creating the 2 subplots
      sns.boxplot(
          data=data, x=feature, ax=ax_box2, showmeans=True, color="violet"
      )  # boxplot will be created and a triangle will indicate the mean value of the column
      sns.histplot(
          data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins
      ) if bins else sns.histplot(
          data=data, x=feature, kde=kde, ax=ax_hist2
      )  # For histogram
      ax_hist2.axvline(
          data[feature].mean(), color="green", linestyle="--"
      )  # Add mean to the histogram
      ax_hist2.axvline(
          data[feature].median(), color="black", linestyle="-"
      )  # Add median to the histogram

  # function to compute adjusted R-squared
  def adj_r2_score(predictors, targets, predictions):
      r2 = r2_score(targets, predictions)
      n = predictors.shape[0]
      k = predictors.shape[1]
      return 1 - ((1 - r2) * (n - 1) / (n - k - 1))


  # function to compute MAPE
  def mape_score(targets, predictions):
      return np.mean(np.abs(targets - predictions) / targets) * 100


  # function to compute different metrics to check performance of a regression model
  def model_performance_regression(model, predictors, target):
      """
      Function to compute different metrics to check regression model performance

      model: regressor
      predictors: independent variables
      target: dependent variable
      """

      # predicting using the independent variables
      pred = model.predict(predictors)

      r2 = r2_score(target, pred)  # to compute R-squared
      adjr2 = adj_r2_score(predictors, target, pred)  # to compute adjusted R-squared
      rmse = np.sqrt(mean_squared_error(target, pred))  # to compute RMSE
      mae = mean_absolute_error(target, pred)  # to compute MAE
      mape = mape_score(target, pred)  # to compute MAPE

      # creating a dataframe of metrics
      df_perf = pd.DataFrame(
          {
              "RMSE": rmse,
              "MAE": mae,
              "R-squared": r2,
              "Adj. R-squared": adjr2,
              "MAPE": mape,
          },
          index=[0],
      )

      return df_perf

  ##  Function to calculate r2_score and RMSE on train and test data
  def get_model_score(model, flag=True):
      '''
      model : classifier to predict values of X

      '''
      # defining an empty list to store train and test results
      score_list=[]

      pred_train = model.predict(X_train)
      pred_test = model.predict(X_test)

      train_r2=metrics.r2_score(y_train,pred_train)
      test_r2=metrics.r2_score(y_test,pred_test)
      train_rmse=np.sqrt(metrics.mean_squared_error(y_train,pred_train))
      test_rmse=np.sqrt(metrics.mean_squared_error(y_test,pred_test))

      #Adding all scores in the list
      score_list.extend((train_r2,test_r2,train_rmse,test_rmse))

      # If the flag is set to True then only the following print statements will be dispayed, the default value is True
      if flag==True:
          print("R-sqaure on training set : ",metrics.r2_score(y_train,pred_train))
          print("R-square on test set : ",metrics.r2_score(y_test,pred_test))
          print("RMSE on training set : ",np.sqrt(metrics.mean_squared_error(y_train,pred_train)))
          print("RMSE on test set : ",np.sqrt(metrics.mean_squared_error(y_test,pred_test)))

      # returning the list with train and test scores
      return score_list

  ## Function to create confusion matrix
  def make_confusion_matrix(model,y_actual,labels=[1, 0]):
      '''
      model : classifier to predict values of X
      y_actual : ground truth

      '''
      y_predict = model.predict(X_test)
      cm=metrics.confusion_matrix( y_actual, y_predict, labels=[0, 1])
      df_cm = pd.DataFrame(cm, index = [i for i in ["Actual - No","Actual - Yes"]],
                    columns = [i for i in ['Predicted - No','Predicted - Yes']])
      group_counts = ["{0:0.0f}".format(value) for value in
                  cm.flatten()]
      group_percentages = ["{0:.2%}".format(value) for value in
                           cm.flatten()/np.sum(cm)]
      labels = [f"{v1}\n{v2}" for v1, v2 in
                zip(group_counts,group_percentages)]
      labels = np.asarray(labels).reshape(2,2)
      plt.figure(figsize = (10,7))
      sns.heatmap(df_cm, annot=labels,fmt='')
      plt.ylabel('True label')
      plt.xlabel('Predicted label')

  ##  Function to calculate different metric scores of the model - Accuracy, Recall and Precision
  def get_metrics_score(model,flag=True):
      '''
      model : classifier to predict values of X

      '''
      # defining an empty list to store train and test results
      score_list=[]

      pred_train = model.predict(X_train)
      pred_test = model.predict(X_test)

      train_acc = model.score(X_train,y_train)
      test_acc = model.score(X_test,y_test)

      train_recall = metrics.recall_score(y_train,pred_train)
      test_recall = metrics.recall_score(y_test,pred_test)

      train_precision = metrics.precision_score(y_train,pred_train)
      test_precision = metrics.precision_score(y_test,pred_test)

      score_list.extend((train_acc,test_acc,train_recall,test_recall,train_precision,test_precision))

      # If the flag is set to True then only the following print statements will be dispayed. The default value is set to True.
      if flag == True:
          print("Accuracy on training set : ",model.score(X_train,y_train))
          print("Accuracy on test set : ",model.score(X_test,y_test))
          print("Recall on training set : ",metrics.recall_score(y_train,pred_train))
          print("Recall on test set : ",metrics.recall_score(y_test,pred_test))
          print("Precision on training set : ",metrics.precision_score(y_train,pred_train))
          print("Precision on test set : ",metrics.precision_score(y_test,pred_test))

      return score_list # returning the list with train and test scores


  from google.colab import drive
  drive.mount('/content/drive')

Mounted at /content/drive


  from urllib.parse import urldefrag
  #read excel file and load sheet 'Data Dict' into dataframe
  vu_dataDict = pd.read_excel('/content/drive/MyDrive/Notebooks/Travel_Package/Data/Tourism.xlsx', sheet_name='Data Dict')

  #read excel file, load sheet "Tourism" as a dataframe
  vu_Tourism = pd.read_excel('/content/drive/MyDrive/Notebooks/Travel_Package/Data/Tourism.xlsx', sheet_name='Tourism')

  #make a copy of the original dataframe
  vu_Tourism_cp = vu_Tourism.copy()


  # computing number of columns, minus the ID that doesn't provide any valuable information
  cols = len(vu_Tourism.axes[1])
  count = 0
  print("These features have null values: ")
  while (count < cols):
      if vu_Tourism[vu_Tourism.columns[count]].isnull().values.sum() != 0 :
        print(vu_Tourism.columns[count], "has ",vu_Tourism[vu_Tourism.columns[count]].isnull().values.sum()," null values")
      count = count + 1

These features have null values:
  Age has  226  null values
  TypeofContact has  25  null values
  DurationOfPitch has  251  null values
  NumberOfFollowups has  45  null values
  PreferredPropertyStar has  26  null values
  NumberOfTrips has  140  null values
  NumberOfChildrenVisiting has  66  null values
  MonthlyIncome has  233  null values


  #shape of Data Dictionary
  vu_dataDict.shape

(21, 4)


  #display all data dict
  vu_dataDict.head(21)


  #shape of Tourism
  vu_Tourism.shape

(4888, 20)


  #visualize first 20 rows of Tourism
  vu_Tourism.head(20)


  #visualize the datatype and if features have null values
  vu_Tourism.info()

<class 'pandas.core.frame.DataFrame'>
  RangeIndex: 4888 entries, 0 to 4887
  Data columns (total 20 columns):
   #   Column                    Non-Null Count  Dtype
  ---  ------                    --------------  -----
   0   CustomerID                4888 non-null   int64
   1   ProdTaken                 4888 non-null   int64
   2   Age                       4662 non-null   float64
   3   TypeofContact             4863 non-null   object
   4   CityTier                  4888 non-null   int64
   5   DurationOfPitch           4637 non-null   float64
   6   Occupation                4888 non-null   object
   7   Gender                    4888 non-null   object
   8   NumberOfPersonVisiting    4888 non-null   int64
   9   NumberOfFollowups         4843 non-null   float64
   10  ProductPitched            4888 non-null   object
   11  PreferredPropertyStar     4862 non-null   float64
   12  MaritalStatus             4888 non-null   object
   13  NumberOfTrips             4748 non-null   float64
   14  Passport                  4888 non-null   int64
   15  PitchSatisfactionScore    4888 non-null   int64
   16  OwnCar                    4888 non-null   int64
   17  NumberOfChildrenVisiting  4822 non-null   float64
   18  Designation               4888 non-null   object
   19  MonthlyIncome             4655 non-null   float64
  dtypes: float64(7), int64(7), object(6)
  memory usage: 763.9+ KB


  #Analyze features
  print(vu_Tourism.ProdTaken.value_counts(),"\n")
  print(vu_Tourism.Age.value_counts(),"\n")
  print(vu_Tourism.TypeofContact.value_counts(),"\n")
  print(vu_Tourism.CityTier.value_counts(),"\n")
  print(vu_Tourism.DurationOfPitch.value_counts(),"\n")
  print(vu_Tourism.Occupation.value_counts(),"\n")
  print(vu_Tourism.Gender.value_counts(),"\n")
  print(vu_Tourism.NumberOfPersonVisiting.value_counts(),"\n")
  print(vu_Tourism.NumberOfFollowups.value_counts(),"\n")
  print(vu_Tourism.ProductPitched.value_counts(),"\n")
  print(vu_Tourism.PreferredPropertyStar.value_counts(),"\n")
  print(vu_Tourism.MaritalStatus.value_counts(),"\n")
  print(vu_Tourism.NumberOfTrips.value_counts(),"\n")
  print(vu_Tourism.Passport.value_counts(),"\n")
  print(vu_Tourism.PitchSatisfactionScore.value_counts(),"\n")
  print(vu_Tourism.OwnCar.value_counts(),"\n")
  print(vu_Tourism.NumberOfChildrenVisiting.value_counts(),"\n")
  print(vu_Tourism.Designation.value_counts(),"\n")
  print(vu_Tourism.MonthlyIncome.value_counts(),"\n")

0    3968
  1     920
  Name: ProdTaken, dtype: int64

  35.0    237
  36.0    231
  34.0    211
  31.0    203
  30.0    199
  32.0    197
  33.0    189
  37.0    185
  29.0    178
  38.0    176
  41.0    155
  39.0    150
  28.0    147
  40.0    146
  42.0    142
  27.0    138
  43.0    130
  46.0    121
  45.0    116
  26.0    106
  44.0    105
  51.0     90
  47.0     88
  50.0     86
  25.0     74
  52.0     68
  53.0     66
  48.0     65
  49.0     65
  55.0     64
  54.0     61
  56.0     58
  24.0     56
  22.0     46
  23.0     46
  59.0     44
  21.0     41
  20.0     38
  19.0     32
  58.0     31
  57.0     29
  60.0     29
  18.0     14
  61.0      9
  Name: Age, dtype: int64

  Self Enquiry       3444
  Company Invited    1419
  Name: TypeofContact, dtype: int64

  1    3190
  3    1500
  2     198
  Name: CityTier, dtype: int64

  9.0      483
  7.0      342
  8.0      333
  6.0      307
  16.0     274
  15.0     269
  14.0     253
  10.0     244
  13.0     223
  11.0     205
  12.0     195
  17.0     172
  30.0      95
  22.0      89
  31.0      83
  23.0      79
  18.0      75
  29.0      74
  32.0      74
  25.0      73
  21.0      73
  27.0      72
  26.0      72
  24.0      70
  35.0      66
  20.0      65
  28.0      61
  33.0      57
  19.0      57
  34.0      50
  36.0      44
  5.0        6
  126.0      1
  127.0      1
  Name: DurationOfPitch, dtype: int64

  Salaried          2368
  Small Business    2084
  Large Business     434
  Free Lancer          2
  Name: Occupation, dtype: int64

  Male       2916
  Female     1817
  Fe Male     155
  Name: Gender, dtype: int64

  3    2402
  2    1418
  4    1026
  1      39
  5       3
  Name: NumberOfPersonVisiting, dtype: int64

  4.0    2068
  3.0    1466
  5.0     768
  2.0     229
  1.0     176
  6.0     136
  Name: NumberOfFollowups, dtype: int64

  Basic           1842
  Deluxe          1732
  Standard         742
  Super Deluxe     342
  King             230
  Name: ProductPitched, dtype: int64

  3.0    2993
  5.0     956
  4.0     913
  Name: PreferredPropertyStar, dtype: int64

  Married      2340
  Divorced      950
  Single        916
  Unmarried     682
  Name: MaritalStatus, dtype: int64

  2.0     1464
  3.0     1079
  1.0      620
  4.0      478
  5.0      458
  6.0      322
  7.0      218
  8.0      105
  19.0       1
  21.0       1
  20.0       1
  22.0       1
  Name: NumberOfTrips, dtype: int64

  0    3466
  1    1422
  Name: Passport, dtype: int64

  3    1478
  5     970
  1     942
  4     912
  2     586
  Name: PitchSatisfactionScore, dtype: int64

  1    3032
  0    1856
  Name: OwnCar, dtype: int64

  1.0    2080
  2.0    1335
  0.0    1082
  3.0     325
  Name: NumberOfChildrenVisiting, dtype: int64

  Executive         1842
  Manager           1732
  Senior Manager     742
  AVP                342
  VP                 230
  Name: Designation, dtype: int64

  20855.0    7
  21288.0    7
  17342.0    7
  21020.0    7
  24950.0    6
            ..
  17074.0    1
  24250.0    1
  21077.0    1
  24714.0    1
  21471.0    1
  Name: MonthlyIncome, Length: 2475, dtype: int64


  #Lets drop CustomerID because it doesn't contribute any valuable information
  vu_Tourism = vu_Tourism.drop('CustomerID', axis=1)


  #statistics of dataset
  vu_Tourism.describe().T


  print(vu_Tourism['ProdTaken'].isnull().values.sum()," null ProdTaken entries")
  print(vu_Tourism.ProdTaken.value_counts(),"\n")

0  null ProdTaken entries
  0    3968
  1     920
  Name: ProdTaken, dtype: int64


  #ProdTaken Distribution
  labeled_barplot(vu_Tourism,'ProdTaken',perc=True)


  vu_Tourism.head()


  #Analyze Feature Age
  print(vu_Tourism['Age'].isnull().values.sum()," null Age entries")
  print(vu_Tourism.Age.value_counts(),"\n")

226  null Age entries
  35.0    237
  36.0    231
  34.0    211
  31.0    203
  30.0    199
  32.0    197
  33.0    189
  37.0    185
  29.0    178
  38.0    176
  41.0    155
  39.0    150
  28.0    147
  40.0    146
  42.0    142
  27.0    138
  43.0    130
  46.0    121
  45.0    116
  26.0    106
  44.0    105
  51.0     90
  47.0     88
  50.0     86
  25.0     74
  52.0     68
  53.0     66
  48.0     65
  49.0     65
  55.0     64
  54.0     61
  56.0     58
  24.0     56
  22.0     46
  23.0     46
  59.0     44
  21.0     41
  20.0     38
  19.0     32
  58.0     31
  57.0     29
  60.0     29
  18.0     14
  61.0      9
  Name: Age, dtype: int64


  #What is the median, mean, and mode of the Age feature
  #Age should we drop, median, or mode?
  print("Age median, mean, mode considering Null Values")
  print("Age median: ",vu_Tourism['Age'].median())
  print("Age mean  : ",vu_Tourism['Age'].mean())
  Age_mode = vu_Tourism['Age'].mode()
  print("Age mode  : ",Age_mode[0],"\n")

Age median, mean, mode considering Null Values
  Age median:  36.0
  Age mean  :  37.62226512226512
  Age mode  :  35.0


  #Visualize Age Distribution plots
  fig, (ax1, ax2) = plt.subplots(1,2, figsize=(16,6))
  fig.suptitle('Age Distribution without Null Values', fontsize=12)
  sns.boxplot(data=vu_Tourism,x='Age', ax=ax1)
  sns.distplot(vu_Tourism.Age,ax=ax2)
  plt.show()


  #Round age to the nearest smallest whole number because even people aged xx.90 will always say they are xx years old.
  age_mean = math.floor(vu_Tourism['Age'].mean())
  age_median = math.floor(vu_Tourism['Age'].median())
  age_mode= math.floor(vu_Tourism['Age'].mode())
  print("Age mean: ",age_mean)
  print("Age median  : ",age_median)
  print("Age mode  : ",age_mode)

Age mean:  37
  Age median  :  36
  Age mode  :  35


  vu_Tourism["Age"].fillna(age_mean, inplace = True)


  print("Age mean  : ",math.floor(vu_Tourism['Age'].mean()))
  print("Age median: ",math.floor(vu_Tourism['Age'].median()))
  age_mode = math.floor(vu_Tourism['Age'].mode())
  print("Age mode  : ",age_mode)

Age mean  :  37
  Age median:  37
  Age mode  :  37


  #Visualize Age Distribution plots After Imputation to Mean
  fig, (ax1, ax2) = plt.subplots(1,2, figsize=(16,6))
  fig.suptitle('Age Distribution after Imputation to Mean', fontsize=12)
  sns.boxplot(data=vu_Tourism,x='Age', ax=ax1)
  sns.distplot(vu_Tourism.Age,ax=ax2)
  plt.show()


  print(vu_Tourism['Age'].isnull().values.sum()," null Age entries")

0  null Age entries


  #Fix Female "Fe Male" to "Female"
  vu_Tourism.loc[vu_Tourism["Gender"] == "Fe Male", "Gender"] = "Female"


  print(vu_Tourism.Gender.value_counts())

Male      2916
  Female    1972
  Name: Gender, dtype: int64


  #Gender distribution
  labeled_barplot(vu_Tourism,"Gender",perc=True)


  #Analyze Feature TypeofContact
  print(vu_Tourism['TypeofContact'].isnull().values.sum()," null TypeofContact entries")
  print(vu_Tourism.TypeofContact.value_counts(),"\n")

25  null TypeofContact entries
  Self Enquiry       3444
  Company Invited    1419
  Name: TypeofContact, dtype: int64


  #Distribution of TypeofContact
  labeled_barplot(vu_Tourism,"TypeofContact",perc=True)


  #number rows with no null TypeofContact
  TypeofContact_noNull=vu_Tourism['TypeofContact'].notnull().sum()

  print("TypeofContact has",TypeofContact_noNull," NOT null values")
  #Calculate the percentages
  SelfInquiryPer= math.ceil((vu_Tourism.TypeofContact.value_counts()[0]*100)/TypeofContact_noNull)
  CompanyInvitedPer = math.floor((vu_Tourism.TypeofContact.value_counts()[1]*100)/TypeofContact_noNull)

  TypeofContact_Null = vu_Tourism['TypeofContact'].isnull().values.sum()

  ImputateToSelf = math.ceil(TypeofContact_Null * SelfInquiryPer/100)
  ImputateToInvited = math.floor(TypeofContact_Null * CompanyInvitedPer/100)
  print ("Percentage of Self Inquiry ",SelfInquiryPer,"% - Imputate ",ImputateToSelf, "null entries as Self Inquiry")
  print("Percentage of Company Invited ",CompanyInvitedPer,"%- Imputate ",ImputateToInvited, "null entries as Company Invited")
  print(ImputateToSelf )
  print(ImputateToInvited)

TypeofContact has 4863  NOT null values
  Percentage of Self Inquiry  71 % - Imputate  18 null entries as Self Inquiry
  Percentage of Company Invited  29 %- Imputate  7 null entries as Company Invited
  18
  7


  #Imputate null values
  vu_Tourism['TypeofContact'].fillna(value="Self Enquiry", inplace=True,limit=ImputateToSelf)
  vu_Tourism['TypeofContact'].fillna(value="Company Invited",inplace=True,limit=ImputateToInvited)

  #Number of null values after imputation
  #Analyze Feature Age including Null entries
  print(vu_Tourism['TypeofContact'].isnull().values.sum()," null TypeofContact entries")
  print(vu_Tourism.TypeofContact.value_counts(),"\n")

0  null TypeofContact entries
  Self Enquiry       3462
  Company Invited    1426
  Name: TypeofContact, dtype: int64


  #Distribution of TypeofContact after null data imputation
  labeled_barplot(vu_Tourism,"TypeofContact",perc=True)


  #Analyze Feature Age including Null entries
  print(vu_Tourism['DurationOfPitch'].isnull().values.sum()," null DurationOfPitch entries")
  print(vu_Tourism.DurationOfPitch.value_counts(),"\n")

251  null DurationOfPitch entries
  9.0      483
  7.0      342
  8.0      333
  6.0      307
  16.0     274
  15.0     269
  14.0     253
  10.0     244
  13.0     223
  11.0     205
  12.0     195
  17.0     172
  30.0      95
  22.0      89
  31.0      83
  23.0      79
  18.0      75
  29.0      74
  32.0      74
  25.0      73
  21.0      73
  27.0      72
  26.0      72
  24.0      70
  35.0      66
  20.0      65
  28.0      61
  33.0      57
  19.0      57
  34.0      50
  36.0      44
  5.0        6
  126.0      1
  127.0      1
  Name: DurationOfPitch, dtype: int64


  #First lets analyze DurationOfPicth with vu_Tourism_noNull that contains no null values

  DOP_mean = vu_Tourism['DurationOfPitch'].mean()
  DOP_median = vu_Tourism['DurationOfPitch'].median()
  DOP_mode = vu_Tourism['DurationOfPitch'].mode()

  print("DurationOfPitch: \n")
  print("mean   : ", math.ceil(DOP_mean))
  print("median : ", DOP_median)
  print("mode   : ", DOP_mode[0])

DurationOfPitch:

  mean   :  16
  median :  13.0
  mode   :  9.0


  #Visualize Duration of Pitch
  fig, (ax1, ax2) = plt.subplots(1,2, figsize=(16,6))
  fig.suptitle('DurationOfPitch', fontsize=12)
  sns.boxplot(data=vu_Tourism,x='DurationOfPitch', ax=ax1)
  sns.distplot(vu_Tourism.DurationOfPitch,ax=ax2)
  plt.show()


  histogram_boxplot(vu_Tourism,'DurationOfPitch')


  labeled_barplot(vu_Tourism,'DurationOfPitch',perc=True)


  #There are outliers 126, and 127.
  DOP_outliers_ceil = vu_Tourism[vu_Tourism['DurationOfPitch']>100]
  DOP_outliers_ceil


  #There are outliers at 5
  DOP_outliers_floor = vu_Tourism[vu_Tourism['DurationOfPitch']<6]
  DOP_outliers_floor


  #drop rows that contain DurationOfPitch >100 or ==5
  DOP_noOutlier_noNull = vu_Tourism[vu_Tourism.DurationOfPitch < 100]
  DOP_noOutlier_noNull = DOP_noOutlier_noNull[DOP_noOutlier_noNull != 5]


  #now we calculate mean, median, and mode for noNull and no Outliers
  DOP_noOutlier_noNull_mean = DOP_noOutlier_noNull['DurationOfPitch'].mean()
  DOP_noOutlier_noNull_median = DOP_noOutlier_noNull['DurationOfPitch'].median()
  DOP_noOutlier_noNull_mode = DOP_noOutlier_noNull['DurationOfPitch'].mode()

  print("DurationOfPitch no Outliers: \n")
  print("mean   : ", math.ceil(DOP_noOutlier_noNull_mean))
  print("median : ", DOP_noOutlier_noNull_median)
  print("mode   : ", DOP_noOutlier_noNull_mode[0])

DurationOfPitch no Outliers:

  mean   :  16
  median :  13.0
  mode   :  9.0


  #Replace DurationOfPitch null values with the mean
  vu_Tourism['DurationOfPitch'].fillna(value=DOP_noOutlier_noNull_mean, inplace=True)
  print("DurationOfPitch has ",vu_Tourism['DurationOfPitch'].isnull().values.sum()," null entries")

  print("DurationOfPitch Mean, Median, Mode: \n")
  print("mean   : ", math.ceil(vu_Tourism['DurationOfPitch'].mean()))
  print("median : ", math.ceil(vu_Tourism['DurationOfPitch'].median()))
  DOP_mode = vu_Tourism['DurationOfPitch'].mode()

  print("mode   : ", DOP_mode[0])

DurationOfPitch has  0  null entries
  DurationOfPitch Mean, Median, Mode:

  mean   :  16
  median :  14
  mode   :  9.0


  #Visualize Duration of Pitch after inputing null values
  fig, (ax1, ax2) = plt.subplots(1,2, figsize=(16,6))
  fig.suptitle('DurationOfPitch', fontsize=12)
  sns.boxplot(data=vu_Tourism,x='DurationOfPitch', ax=ax1)
  sns.distplot(vu_Tourism.DurationOfPitch,ax=ax2)
  plt.show()


  histogram_boxplot(vu_Tourism,'DurationOfPitch')


  #Analyze Feature NumberOfFollowups including Null entries
  print(vu_Tourism['NumberOfFollowups'].isnull().values.sum()," null NumberOfFollowups entries")
  print(vu_Tourism.NumberOfFollowups.value_counts(),"\n")

45  null NumberOfFollowups entries
  4.0    2068
  3.0    1466
  5.0     768
  2.0     229
  1.0     176
  6.0     136
  Name: NumberOfFollowups, dtype: int64


  #Calculate mean, median, mode for the NumberOfFollowups w/o null values

  NOFu_mean = vu_Tourism['NumberOfFollowups'].mean()
  NOFu_median = vu_Tourism['NumberOfFollowups'].median()
  NOFu_mode = vu_Tourism['NumberOfFollowups'].mode()

  print("NumberOfFollowups: \n")
  print("mean   : ", math.ceil(NOFu_mean))
  print("median : ", NOFu_median)

  print("mode   : ", NOFu_mode[0])

NumberOfFollowups:

  mean   :  4
  median :  4.0
  mode   :  4.0


  histogram_boxplot(vu_Tourism,'NumberOfFollowups')


  #Replace NumberOfFollowups null values with the mean
  vu_Tourism['NumberOfFollowups'].fillna(value=NOFu_mean, inplace=True)
  print("NumberOfFollowups has ",vu_Tourism['NumberOfFollowups'].isnull().values.sum()," null entries")

NumberOfFollowups has  0  null entries


  #Analyze Feature Occupation including Null entries
  print(vu_Tourism['Occupation'].isnull().values.sum()," null Occupation entries")
  print(vu_Tourism.Occupation.value_counts(),"\n")

0  null Occupation entries
  Salaried          2368
  Small Business    2084
  Large Business     434
  Free Lancer          2
  Name: Occupation, dtype: int64


  #General Distribution of Customers
  labeled_barplot(vu_Tourism,'Occupation',perc=True)
  plt.show()


  #Analyze Feature PreferredPropertyStar including Null entries
  print(vu_Tourism['PreferredPropertyStar'].isnull().values.sum()," null PreferredPropertyStar entries")
  print(vu_Tourism.PreferredPropertyStar.value_counts(),"\n")

26  null PreferredPropertyStar entries
  3.0    2993
  5.0     956
  4.0     913
  Name: PreferredPropertyStar, dtype: int64


  #lets see the distribution for PreferredPropertyStar w/ Null values
  labeled_barplot(vu_Tourism,'PreferredPropertyStar',perc=True)


  #mode of PreferredPropertyStar
  PPS_mode = vu_Tourism['PreferredPropertyStar'].mode()
  PPS_mode = PPS_mode[0]
  print("PreferredPropertyStar mode  : ",PPS_mode)

PreferredPropertyStar mode  :  3.0


  #imputate mode into the missing values for PreferredPropertyStar
  values = {"PreferredPropertyStar": PPS_mode}
  vu_Tourism.fillna(value=values,inplace=True)


  #Analyze Feature PreferredPropertyStar including Null entries
  print(vu_Tourism['PreferredPropertyStar'].isnull().values.sum()," null PreferredPropertyStar entries")
  print(vu_Tourism.PreferredPropertyStar.value_counts(),"\n")

0  null PreferredPropertyStar entries
  3.0    3019
  5.0     956
  4.0     913
  Name: PreferredPropertyStar, dtype: int64


  #Analyze Feature NumberOfTrips including Null entries
  print(vu_Tourism['NumberOfTrips'].isnull().values.sum()," null NumberOfTrips entries")
  print(vu_Tourism.NumberOfTrips.value_counts(),"\n")

140  null NumberOfTrips entries
  2.0     1464
  3.0     1079
  1.0      620
  4.0      478
  5.0      458
  6.0      322
  7.0      218
  8.0      105
  19.0       1
  21.0       1
  20.0       1
  22.0       1
  Name: NumberOfTrips, dtype: int64


  print("NumberOfTrips median, mean, mode without Null Values")
  print("NumberOfTrips median: ",vu_Tourism['NumberOfTrips'].median())
  print("NumberOfTrips  : ",vu_Tourism['NumberOfTrips'].mean())
  NOT_mode = vu_Tourism['NumberOfTrips'].mode()
  print("NumberOfTrips  : ",NOT_mode[0])

NumberOfTrips median, mean, mode without Null Values
  NumberOfTrips median:  3.0
  NumberOfTrips  :  3.236520640269587
  NumberOfTrips  :  2.0


  #NumberOfTrips distribution
  labeled_barplot(vu_Tourism,'NumberOfTrips',perc=True)


  histogram_boxplot(vu_Tourism,'NumberOfTrips')


  #Analyze Feature NumberOfChildrenVisiting including Null entries
  print(vu_Tourism['NumberOfChildrenVisiting'].isnull().values.sum()," null NumberOfChildrenVisiting entries")
  print(vu_Tourism.NumberOfChildrenVisiting.value_counts(),"\n")

66  null NumberOfChildrenVisiting entries
  1.0    2080
  2.0    1335
  0.0    1082
  3.0     325
  Name: NumberOfChildrenVisiting, dtype: int64


  print("NumberOfChildrenVisiting median, mean, mode without Null Values")
  print("NumberOfChildrenVisiting median: ",vu_Tourism['NumberOfChildrenVisiting'].median())
  print("NumberOfChildrenVisiting mean : ",round(vu_Tourism['NumberOfChildrenVisiting'].mean(),1))
  NOCV_mode = vu_Tourism['NumberOfChildrenVisiting'].mode()
  print("NumberOfChildrenVisiting  : ",NOCV_mode[0])

NumberOfChildrenVisiting median, mean, mode without Null Values
  NumberOfChildrenVisiting median:  1.0
  NumberOfChildrenVisiting mean :  1.2
  NumberOfChildrenVisiting  :  1.0


  #Distribution of NumberOfChildrenVisiting
  labeled_barplot(vu_Tourism,'NumberOfChildrenVisiting',perc=True)


  #imputate mode into the missing values for NumberOfChildrenVisiting
  NOCV_mode = 1
  values = {"NumberOfChildrenVisiting": NOCV_mode}
  vu_Tourism.fillna(value=values,inplace=True)


  print(vu_Tourism['NumberOfChildrenVisiting'].isnull().values.sum()," null NumberOfChildrenVisiting entries")
  print(vu_Tourism.NumberOfChildrenVisiting.value_counts(),"\n")

0  null NumberOfChildrenVisiting entries
  1.0    2146
  2.0    1335
  0.0    1082
  3.0     325
  Name: NumberOfChildrenVisiting, dtype: int64


  #Analyze MaritalStatus including Null entries
  print(vu_Tourism['MaritalStatus'].isnull().values.sum()," null MaritalStatus entries")
  print(vu_Tourism.MaritalStatus.value_counts(),"\n")

0  null MaritalStatus entries
  Married      2340
  Divorced      950
  Single        916
  Unmarried     682
  Name: MaritalStatus, dtype: int64


  labeled_barplot(vu_Tourism,'MaritalStatus',perc=True)


  #Analyze Passport including Null entries
  print(vu_Tourism['Passport'].isnull().values.sum()," null Passport entries")
  print(vu_Tourism.Passport.value_counts(),"\n")

0  null Passport entries
  0    3466
  1    1422
  Name: Passport, dtype: int64


  labeled_barplot(vu_Tourism,'Passport',perc=True)


  #Analyze PitchSatisfactionScore including Null entries
  print(vu_Tourism['PitchSatisfactionScore'].isnull().values.sum()," null PitchSatisfactionScore entries")
  print(vu_Tourism.PitchSatisfactionScore.value_counts(),"\n")

0  null PitchSatisfactionScore entries
  3    1478
  5     970
  1     942
  4     912
  2     586
  Name: PitchSatisfactionScore, dtype: int64


  labeled_barplot(vu_Tourism,"PitchSatisfactionScore",perc=True)


  #Analyze OwnCar including Null entries
  print(vu_Tourism['OwnCar'].isnull().values.sum()," null OwnCar entries")
  print(vu_Tourism.OwnCar.value_counts(),"\n")

0  null OwnCar entries
  1    3032
  0    1856
  Name: OwnCar, dtype: int64


  labeled_barplot(vu_Tourism,"OwnCar",perc=True)


  #Analyze CityTier including Null entries
  print(vu_Tourism['CityTier'].isnull().values.sum()," null CityTier entries")
  print(vu_Tourism.CityTier.value_counts(),"\n")

0  null CityTier entries
  1    3190
  3    1500
  2     198
  Name: CityTier, dtype: int64


  labeled_barplot(vu_Tourism,"CityTier",perc=True)


  #Analyze MonthlyIncome including Null entries
  print(vu_Tourism['MonthlyIncome'].isnull().values.sum()," null MonthlyIncome entries")
  print(vu_Tourism.MonthlyIncome.value_counts(),"\n")

233  null MonthlyIncome entries
  20855.0    7
  21288.0    7
  17342.0    7
  21020.0    7
  24950.0    6
            ..
  17074.0    1
  24250.0    1
  21077.0    1
  24714.0    1
  21471.0    1
  Name: MonthlyIncome, Length: 2475, dtype: int64


  #Visualize MonthlyIncome Distribution
  fig, (ax1, ax2) = plt.subplots(1,2, figsize=(16,6))
  fig.suptitle('MonthlyIncome', fontsize=12)
  sns.boxplot(data=vu_Tourism,x='MonthlyIncome', ax=ax1)
  sns.distplot(vu_Tourism.MonthlyIncome,ax=ax2)
  plt.show()


  histogram_boxplot(vu_Tourism,'MonthlyIncome')


  #The largest MonthlyIncome
  vu_Tourism['MonthlyIncome'].nlargest()

2482    98678.0
  38      95000.0
  2634    38677.0
  4104    38677.0
  3190    38651.0
  Name: MonthlyIncome, dtype: float64


  #smallest MonthlyIncome
  vu_Tourism['MonthlyIncome'].nsmallest()

142      1000.0
  2586     4678.0
  513     16009.0
  1983    16009.0
  727     16051.0
  Name: MonthlyIncome, dtype: float64


  MI_noOutliers = vu_Tourism[(vu_Tourism['MonthlyIncome']>15000) & (vu_Tourism['MonthlyIncome'] < 40000)]
  MI_noOutliers_mean = round(MI_noOutliers['MonthlyIncome'].mean(),0)
  print("The MonthlyIncome mean w/o Outliers is ",MI_noOutliers_mean)

The MonthlyIncome mean w/o Outliers is  23597.0


  values = {"MonthlyIncome": MI_noOutliers_mean}
  vu_Tourism.fillna(value=values,inplace=True)


  print(vu_Tourism['MonthlyIncome'].isnull().values.sum()," null MonthlyIncome entries")
  print(vu_Tourism.MonthlyIncome.value_counts(),"\n")

0  null MonthlyIncome entries
  23597.0    233
  20855.0      7
  21020.0      7
  21288.0      7
  17342.0      7
            ...
  17074.0      1
  24250.0      1
  21077.0      1
  24714.0      1
  21471.0      1
  Name: MonthlyIncome, Length: 2476, dtype: int64


  #Analyze Designation including Null entries
  print(vu_Tourism['Designation'].isnull().values.sum()," null Designation entries")
  print(vu_Tourism.Designation.value_counts(),"\n")

0  null Designation entries
  Executive         1842
  Manager           1732
  Senior Manager     742
  AVP                342
  VP                 230
  Name: Designation, dtype: int64


  labeled_barplot(vu_Tourism,"Designation",perc=True)


  #Analyze ProductPitched including Null entries
  print(vu_Tourism['ProductPitched'].isnull().values.sum()," null ProductPitchedentries")
  print(vu_Tourism.ProductPitched.value_counts(),"\n")

0  null ProductPitchedentries
  Basic           1842
  Deluxe          1732
  Standard         742
  Super Deluxe     342
  King             230
  Name: ProductPitched, dtype: int64


  labeled_barplot(vu_Tourism,"ProductPitched",perc=True)


  #lets look at the correlation between features
  print(vu_Tourism.corr())

                          ProdTaken       Age  CityTier  DurationOfPitch  \
  ProdTaken                  1.000000 -0.143755  0.086852         0.076302
  Age                       -0.143755  1.000000 -0.014352        -0.011178
  CityTier                   0.086852 -0.014352  1.000000         0.021985
  DurationOfPitch            0.076302 -0.011178  0.021985         1.000000
  NumberOfPersonVisiting     0.009627  0.013817 -0.001671         0.063448
  NumberOfFollowups          0.111675 -0.000778  0.023524         0.009291
  PreferredPropertyStar      0.098704 -0.015734 -0.008479        -0.005915
  NumberOfTrips              0.018898  0.181294 -0.029709         0.009637
  Passport                   0.260844  0.032529  0.001793         0.032264
  PitchSatisfactionScore     0.051394  0.017803 -0.042160        -0.002806
  OwnCar                    -0.011508  0.047468  0.003817        -0.001598
  NumberOfChildrenVisiting   0.008009  0.005279  0.000578         0.030568
  MonthlyIncome             -0.128065  0.457561  0.050666        -0.005888

                            NumberOfPersonVisiting  NumberOfFollowups  \
  ProdTaken                               0.009627           0.111675
  Age                                     0.013817          -0.000778
  CityTier                               -0.001671           0.023524
  DurationOfPitch                         0.063448           0.009291
  NumberOfPersonVisiting                  1.000000           0.326649
  NumberOfFollowups                       0.326649           1.000000
  PreferredPropertyStar                   0.030519          -0.027149
  NumberOfTrips                           0.195223           0.138783
  Passport                                0.011177           0.004951
  PitchSatisfactionScore                 -0.019581           0.004035
  OwnCar                                  0.010362           0.012057
  NumberOfChildrenVisiting                0.606321           0.282767
  MonthlyIncome                           0.189448           0.172689

                            PreferredPropertyStar  NumberOfTrips  Passport  \
  ProdTaken                              0.098704       0.018898  0.260844
  Age                                   -0.015734       0.181294  0.032529
  CityTier                              -0.008479      -0.029709  0.001793
  DurationOfPitch                       -0.005915       0.009637  0.032264
  NumberOfPersonVisiting                 0.030519       0.195223  0.011177
  NumberOfFollowups                     -0.027149       0.138783  0.004951
  PreferredPropertyStar                  1.000000       0.009483  0.001221
  NumberOfTrips                          0.009483       1.000000  0.012949
  Passport                               0.001221       0.012949  1.000000
  PitchSatisfactionScore                -0.024019      -0.004378  0.002926
  OwnCar                                 0.013577      -0.011825 -0.022330
  NumberOfChildrenVisiting               0.031362       0.166463  0.020244
  MonthlyIncome                          0.000796       0.134595  0.002503

                            PitchSatisfactionScore    OwnCar  \
  ProdTaken                               0.051394 -0.011508
  Age                                     0.017803  0.047468
  CityTier                               -0.042160  0.003817
  DurationOfPitch                        -0.002806 -0.001598
  NumberOfPersonVisiting                 -0.019581  0.010362
  NumberOfFollowups                       0.004035  0.012057
  PreferredPropertyStar                  -0.024019  0.013577
  NumberOfTrips                          -0.004378 -0.011825
  Passport                                0.002926 -0.022330
  PitchSatisfactionScore                  1.000000  0.068850
  OwnCar                                  0.068850  1.000000
  NumberOfChildrenVisiting                0.000251  0.025659
  MonthlyIncome                           0.029688  0.078309

                            NumberOfChildrenVisiting  MonthlyIncome
  ProdTaken                                 0.008009      -0.128065
  Age                                       0.005279       0.457561
  CityTier                                  0.000578       0.050666
  DurationOfPitch                           0.030568      -0.005888
  NumberOfPersonVisiting                    0.606321       0.189448
  NumberOfFollowups                         0.282767       0.172689
  PreferredPropertyStar                     0.031362       0.000796
  NumberOfTrips                             0.166463       0.134595
  Passport                                  0.020244       0.002503
  PitchSatisfactionScore                    0.000251       0.029688
  OwnCar                                    0.025659       0.078309
  NumberOfChildrenVisiting                  1.000000       0.182505
  MonthlyIncome                             0.182505       1.000000


  sns.pairplot(vu_Tourism,hue='ProdTaken')
  plt.show()


  sns.set(rc={'figure.figsize':(24,24)})
  sns.heatmap(vu_Tourism.corr(), annot=True, linewidths=.5, center=0, cbar=False, cmap="Spectral")
  plt.show()


  #Lets see NumberOfTrips vs ProdTaken (Yes and No)
  plt.figure(figsize=(12,8), dpi= 80)
  sns.kdeplot(vu_Tourism.loc[vu_Tourism['ProdTaken'] == 1, "NumberOfTrips"], shade=True, color="g", label="ProdTaken=Yes", alpha=.7)
  sns.kdeplot(vu_Tourism.loc[vu_Tourism['ProdTaken'] == 0, "NumberOfTrips"], shade=True, color="deeppink", label="ProdTaken=No", alpha=.7)
  plt.title('Density NumberOfTrips for ProdTaken', fontsize=22)
  plt.legend()
  plt.show()


  #Lets look at ProdTaken=Yes, NumberOfTrips, and Age

  PT_yes = vu_Tourism[vu_Tourism['ProdTaken']==1]
  PT_no = vu_Tourism[vu_Tourism['ProdTaken']==0]

  #Lets see NumberOfTrips vs ProdTaken (Yes and No)
  sns.set(rc={'figure.figsize':(18,7)})
  sns.catplot(x="Age", y="NumberOfTrips", kind="swarm", data=PT_yes, height=7, aspect=3);
  plt.title("ProdTaken=Yes, Age vs NumberOfTrips", fontsize=16)

  sns.set(rc={'figure.figsize':(18,7)})
  sns.catplot(x="Age", y="NumberOfTrips", kind="swarm", data=PT_yes, height=7, aspect=3);
  plt.title("ProdTaken=No, Age vs NumberOfTrips", fontsize=16)

  plt.show()


  #Lets look at ProdTaken=Yes, NumberOfTrips, and NumberOfFollowups
  #Remember PT_Yes and PT_No were defined earlier

  #Lets see NumberOfTrips vs ProdTaken (Yes and No)
  sns.set(rc={'figure.figsize':(12,6)})
  sns.catplot(x="NumberOfFollowups", y="NumberOfTrips", kind="swarm", data=PT_yes, height=7, aspect=3);
  plt.title("ProdTaken=Yes, NumberOfFollowups vs NumberOfTrips", fontsize=16)

  sns.set(rc={'figure.figsize':(12,6)})
  sns.catplot(x="NumberOfFollowups", y="NumberOfTrips", kind="swarm", data=PT_yes, height=7, aspect=3);
  plt.title("ProdTaken=No, NumberOfFollowups vs NumberOfTrips", fontsize=16)

  plt.show()


  #Lets look at ProdTaken=Yes, NumberOfTrips, and NumberOfChildrenVisiting
  #Remember PT_Yes and PT_No were defined earlier

  #Lets see NumberOfChildrenVisiting vs ProdTaken (Yes and No)
  sns.set(rc={'figure.figsize':(12,6)})
  sns.catplot(x="NumberOfChildrenVisiting", y="NumberOfTrips", kind="swarm", data=PT_yes, height=7, aspect=3);
  plt.title("ProdTaken=Yes, NumberOfChildrenVisiting vs NumberOfTrips", fontsize=16)

  sns.set(rc={'figure.figsize':(12,6)})
  sns.catplot(x="NumberOfChildrenVisiting", y="NumberOfTrips", kind="swarm", data=PT_yes, height=7, aspect=3);
  plt.title("ProdTaken=No, NumberOfChildrenVisiting vs NumberOfTrips", fontsize=16)

  plt.show()


  #Lets look at ProdTaken=Yes, NumberOfTrips, and NumberOfPersonVisiting
  #Remember PT_Yes and PT_No were defined earlier

  #Lets see NumberOfPersonVisiting vs ProdTaken (Yes and No)
  sns.set(rc={'figure.figsize':(12,6)})
  sns.catplot(x="NumberOfPersonVisiting", y="NumberOfTrips", kind="swarm", data=PT_yes, height=7, aspect=3);
  plt.title("ProdTaken=Yes, NumberOfPersonVisiting vs NumberOfTrips", fontsize=16)

  sns.set(rc={'figure.figsize':(12,6)})
  sns.catplot(x="NumberOfPersonVisiting", y="NumberOfTrips", kind="swarm", data=PT_yes, height=7, aspect=3);
  plt.title("ProdTaken=No, NumberOfPersonVisiting vs NumberOfTrips", fontsize=16)

  plt.show()


  #Lets look at ProdTaken=Yes, NumberOfTrips, and MonthlyIncome
  #Remember PT_Yes and PT_No were defined earlier

  #Lets see NumberOfTrips vs ProdTaken (Yes and No)
  sns.set(rc={'figure.figsize':(18,7)})
  sns.catplot(x="MonthlyIncome", y="NumberOfTrips", kind="swarm", data=PT_yes, height=7, aspect=3);
  plt.title("ProdTaken=Yes, MonthlyIncome vs NumberOfTrips", fontsize=16)

  sns.set(rc={'figure.figsize':(18,7)})
  sns.catplot(x="MonthlyIncome", y="NumberOfTrips", kind="swarm", data=PT_yes, height=7, aspect=3);
  plt.title("ProdTaken=No, MonthlyIncome vs NumberOfTrips", fontsize=16)

  plt.show()


  #imputate 2 into the missing values for NumberOfTrips
  NOT_atLeast = 3
  values = {"NumberOfTrips": NOT_atLeast}
  vu_Tourism.fillna(value=values,inplace=True)


  vu_Tourism.info()

<class 'pandas.core.frame.DataFrame'>
  RangeIndex: 4888 entries, 0 to 4887
  Data columns (total 19 columns):
   #   Column                    Non-Null Count  Dtype
  ---  ------                    --------------  -----
   0   ProdTaken                 4888 non-null   int64
   1   Age                       4888 non-null   float64
   2   TypeofContact             4888 non-null   object
   3   CityTier                  4888 non-null   int64
   4   DurationOfPitch           4888 non-null   float64
   5   Occupation                4888 non-null   object
   6   Gender                    4888 non-null   object
   7   NumberOfPersonVisiting    4888 non-null   int64
   8   NumberOfFollowups         4888 non-null   float64
   9   ProductPitched            4888 non-null   object
   10  PreferredPropertyStar     4888 non-null   float64
   11  MaritalStatus             4888 non-null   object
   12  NumberOfTrips             4888 non-null   float64
   13  Passport                  4888 non-null   int64
   14  PitchSatisfactionScore    4888 non-null   int64
   15  OwnCar                    4888 non-null   int64
   16  NumberOfChildrenVisiting  4888 non-null   float64
   17  Designation               4888 non-null   object
   18  MonthlyIncome             4888 non-null   float64
  dtypes: float64(7), int64(6), object(6)
  memory usage: 725.7+ KB


  # outlier detection using boxplot
  numeric_columns = vu_Tourism.select_dtypes(include=np.number).columns.tolist()

  plt.figure(figsize=(15, 12))

  for i, variable in enumerate(numeric_columns):
      plt.subplot(8, 4, i + 1)
      plt.boxplot(vu_Tourism[variable], whis=1.5)
      plt.tight_layout()
      plt.title(variable)

  plt.show()


  #Visualize DurationOfPitch Distribution plots
  fig, (ax1, ax2) = plt.subplots(1,2, figsize=(16,6))
  fig.suptitle('DurationOfPitch', fontsize=12)
  sns.boxplot(data=vu_Tourism,x='DurationOfPitch', ax=ax1)
  sns.distplot(vu_Tourism.DurationOfPitch,ax=ax2)
  plt.show()


  #The largest DurationOfPitch
  vu_Tourism['DurationOfPitch'].nlargest()

3878    127.0
  1434    126.0
  2505     36.0
  2585     36.0
  2643     36.0
  Name: DurationOfPitch, dtype: float64


  DOP_morethan40 = vu_Tourism[vu_Tourism['DurationOfPitch']>40]
  DOP_morethan40


  #Dataframe for ProdTaken Yes and Prodtaken No
  PT_yes = vu_Tourism[vu_Tourism['ProdTaken']==1]
  PT_no = vu_Tourism[vu_Tourism['ProdTaken']==0]


  #How are Age affected by ProdTaken Yes/No
  fig, (ax1, ax2) = plt.subplots(1,2, figsize=(16,6))
  fig.suptitle('Age vs ProdTaken', fontsize=16)

  sns.countplot(data=PT_yes, x='Age', ax=ax1)
  ax1.title.set_text('ProdTaken=YES')

  sns.countplot(data=PT_no, x='Age', ax=ax2)
  ax2.title.set_text('ProdTaken=NO')

  plt.show()


  #How are TypeOfContact affected by ProdTaken Yes/No
  fig, (ax1, ax2) = plt.subplots(1,2, figsize=(24,8))
  fig.suptitle('TypeofContact vs ProdTaken', fontsize=16)

  sns.countplot(data=PT_yes, x='TypeofContact', ax=ax1)
  ax1.title.set_text('ProdTaken=YES')

  sns.countplot(data=PT_no, x='TypeofContact', ax=ax2)
  ax2.title.set_text('ProdTaken=NO')

  plt.show()


  #How are CityTier affected by ProdTaken Yes/No

  fig, (ax1, ax2) = plt.subplots(1,2, figsize=(24,8))
  fig.suptitle('CityTier vs ProdTaken', fontsize=16)

  sns.countplot(data=PT_yes, x='CityTier', ax=ax1)
  ax1.title.set_text('ProdTaken=YES')

  sns.countplot(data=PT_no, x='CityTier', ax=ax2)
  ax2.title.set_text('ProdTaken=NO')

  plt.show()


  #How are Occupations distributed overall, ProdTaken=Yes, and ProdTaken=No
  fig, (ax1, ax2) = plt.subplots(1,2, figsize=(24,8))
  fig.suptitle('Distribution Occupations vs ProdTaken', fontsize=16)

  sns.countplot(data=PT_yes, x='Occupation', ax=ax1)
  ax1.title.set_text('ProdTaken=YES')

  sns.countplot(data=PT_no, x='Occupation', ax=ax2)
  ax2.title.set_text('ProdTaken=NO')

  plt.show()


  #Gender vs ProdTaken Yes/No
  fig, (ax1, ax2) = plt.subplots(1,2, figsize=(16,6))
  fig.suptitle('Gender vs ProdTaken', fontsize=16)

  sns.countplot(data=PT_yes, x='Gender', ax=ax1)
  ax1.title.set_text('ProdTaken=YES')
  sns.countplot(data=PT_no, x='Gender', ax=ax2)
  ax2.title.set_text('ProdTaken=NO')

  plt.show()


  sns.set(rc={'figure.figsize':(21,7)})
  sns.countplot(data=vu_Tourism,x="Age",hue="ProductPitched").set(title='Age vs ProductPitched for ProdTaken=Yes')

[Text(0.5, 1.0, 'Age vs ProductPitched for ProdTaken=Yes')]


  #How are NumberOfPersonVisiting affected by ProdTaken Yes/No
  fig, (ax1, ax2) = plt.subplots(1,2, figsize=(16,6))
  fig.suptitle('NumberOfPersonVisiting vs ProdTaken', fontsize=16)

  sns.countplot(data=PT_yes, x='NumberOfPersonVisiting', ax=ax1)
  ax1.title.set_text('ProdTaken=YES')

  sns.countplot(data=PT_no, x='NumberOfPersonVisiting', ax=ax2)
  ax2.title.set_text('ProdTaken=NO')

  plt.show()


  #PreferredPropertyStar vs ProdTaken
  #How are PreferredPropertyStar distributed vs, ProdTaken=Yes, and ProdTaken=No
  fig, (ax1, ax2) = plt.subplots(1,2, figsize=(24,8))
  fig.suptitle('Distribution PreferredPropertyStar vs ProdTaken', fontsize=16)

  sns.countplot(data=PT_yes, x='PreferredPropertyStar', ax=ax1)
  ax1.title.set_text('ProdTaken=YES')

  sns.countplot(data=PT_no, x='PreferredPropertyStar', ax=ax2)
  ax2.title.set_text('ProdTaken=NO')

  plt.show()


  #MaritalStatus vs ProdTaken
  #How are MaritalStatus distributed vs ProdTaken=Yes, and ProdTaken=No
  fig, (ax1, ax2) = plt.subplots(1,2, figsize=(24,8))
  fig.suptitle('Distribution MaritalStatus vs ProdTaken', fontsize=16)

  sns.countplot(data=PT_yes, x='MaritalStatus', ax=ax1)
  ax1.title.set_text('ProdTaken=YES')

  sns.countplot(data=PT_no, x='MaritalStatus', ax=ax2)
  ax2.title.set_text('ProdTaken=NO')

  plt.show()


  #NumberOfTrips vs ProdTaken
  #How are NumberOfTrips distributed vsProdTaken=Yes, and ProdTaken=No
  fig, (ax1, ax2) = plt.subplots(1,2, figsize=(24,8))
  fig.suptitle('Distribution NumberOfTrips vs ProdTaken', fontsize=16)

  sns.countplot(data=PT_yes, x='NumberOfTrips', ax=ax1)
  ax1.title.set_text('ProdTaken=YES')

  sns.countplot(data=PT_no, x='NumberOfTrips', ax=ax2)
  ax2.title.set_text('ProdTaken=NO')

  plt.show()


  #Passport vs ProdTaken
  #How are Passport distributed vs, ProdTaken=Yes, and ProdTaken=No
  fig, (ax1, ax2) = plt.subplots(1,2, figsize=(24,8))
  fig.suptitle('Passport vs ProdTaken', fontsize=16)

  sns.countplot(data=PT_yes, x='Passport', ax=ax1)
  ax1.title.set_text('ProdTaken=YES')

  sns.countplot(data=PT_no, x='Passport', ax=ax2)
  ax2.title.set_text('ProdTaken=NO')

  plt.show()


  #OwnCar vs ProdTaken
  #How are Own Car distributed vs ProdTaken=Yes, and ProdTaken=No
  fig, (ax1, ax2) = plt.subplots(1,2, figsize=(24,8))
  fig.suptitle('OwnCar vs ProdTaken', fontsize=16)

  sns.countplot(data=PT_yes, x='OwnCar', ax=ax1)
  ax1.title.set_text('ProdTaken=YES')

  sns.countplot(data=PT_no, x='OwnCar', ax=ax2)
  ax2.title.set_text('ProdTaken=NO')

  plt.show()


  #How are NumberOfChildrenVisiting  affected by ProdTaken Yes/No
  fig, (ax1, ax2) = plt.subplots(1,2, figsize=(16,6))
  fig.suptitle('NumberOfChildrenVisiting vs ProdTaken', fontsize=16)

  sns.countplot(data=PT_yes, x='NumberOfChildrenVisiting', ax=ax1)
  ax1.title.set_text('ProdTaken=YES')

  sns.countplot(data=PT_no, x='NumberOfChildrenVisiting', ax=ax2)
  ax2.title.set_text('ProdTaken=NO')

  plt.show()


  #How about NumberOfFollowups and NumberOfChildrenVisiting

  fig, axes = plt.subplots(1, 2, sharex=True, figsize=(10,5))
  fig.suptitle('Number of Followups vs NumberOfChildrenVisiting')


  sns.catplot(ax=axes[0], x="NumberOfFollowups", y="NumberOfChildrenVisiting",
              hue="ProdTaken",data=PT_yes, orient="v", height=5, aspect=1,
              palette="tab10", kind="violin", dodge=True, cut=0, bw=.2)
  axes[0].set_title('ProdTaken=YES')


  sns.catplot(ax=axes[1],x="NumberOfFollowups", y="NumberOfChildrenVisiting",hue="ProdTaken",data=PT_no,
              orient="v", height=5, aspect=1, palette="tab10", kind="violin", dodge=True, cut=0, bw=.2)
  axes[1].set_title('ProdTaken=NO')

  plt.close(0)
  plt.close(1)
  plt.show()


  plt.figure(figsize=(16,10), dpi= 80)
  sns.kdeplot(PT_yes['MonthlyIncome'], shade=True, color="blue", label="ProdTaken=Yes", alpha=.7)
  sns.kdeplot(PT_no['MonthlyIncome'], shade=True, color="red", label="ProdTaken=No", alpha=.7)

  # Decoration
  plt.title('Density MonthlyIncome vs ProdTaken', fontsize=22)
  plt.legend()
  plt.show()


  #How are NumberOfFollowups affected by ProdTaken Yes/No
  fig, (ax1, ax2) = plt.subplots(1,2, figsize=(16,6))
  fig.suptitle('NumberOfFollowups vs ProdTaken', fontsize=16)

  sns.countplot(data=PT_yes, x='NumberOfFollowups', ax=ax1)
  ax1.title.set_text('ProdTaken=YES')

  sns.countplot(data=PT_no, x='NumberOfFollowups', ax=ax2)
  ax2.title.set_text('ProdTaken=NO')

  plt.show()


  #How are PitchSatisfactionScore affected by ProdTaken Yes/No
  fig, (ax1, ax2) = plt.subplots(1,2, figsize=(16,6))
  fig.suptitle('PitchSatisfactionScore vs ProdTaken', fontsize=16)

  sns.countplot(data=PT_yes, x='PitchSatisfactionScore', ax=ax1)
  ax1.title.set_text('ProdTaken=YES')

  sns.countplot(data=PT_no, x='PitchSatisfactionScore', ax=ax2)
  ax2.title.set_text('ProdTaken=NO')

  plt.show()


  #How are ProductPitched affected by ProdTaken Yes/No
  fig, (ax1, ax2) = plt.subplots(1,2, figsize=(16,6))
  fig.suptitle('ProductPitched vs ProdTaken', fontsize=16)

  sns.countplot(data=PT_yes, x='ProductPitched', ax=ax1)
  ax1.title.set_text('ProdTaken=YES')

  sns.countplot(data=PT_no, x='ProductPitched', ax=ax2)
  ax2.title.set_text('ProdTaken=NO')

  plt.show()


  #How are DurationOfPitch affected by ProdTaken Yes/No
  fig, (ax1, ax2) = plt.subplots(1,2, figsize=(16,6))
  fig.suptitle('DurationOfPitch vs ProdTaken', fontsize=16)

  sns.countplot(data=PT_yes, x='DurationOfPitch', ax=ax1)
  ax1.title.set_text('ProdTaken=YES')

  sns.countplot(data=PT_no, x='DurationOfPitch', ax=ax2)
  ax2.title.set_text('ProdTaken=NO')

  plt.show()


  #We will make a copy to use to develop our models
  vu_Tourism_df = vu_Tourism.copy()


  #convert Categorical features to Numerical
  vu_Tourism_df = pd.get_dummies(vu_Tourism_df, columns= ['Gender'], drop_first=True)
  vu_Tourism_df = pd.get_dummies(vu_Tourism_df, columns= ['Occupation'])
  vu_Tourism_df = pd.get_dummies(vu_Tourism_df, columns= ['TypeofContact'], drop_first=True)
  vu_Tourism_df = pd.get_dummies(vu_Tourism_df, columns= ['MaritalStatus'])
  vu_Tourism_df = pd.get_dummies(vu_Tourism_df, columns= ['Designation'])
  vu_Tourism_df = pd.get_dummies(vu_Tourism_df, columns= ['ProductPitched'])


  vu_Tourism_df.info()

<class 'pandas.core.frame.DataFrame'>
  RangeIndex: 4888 entries, 0 to 4887
  Data columns (total 33 columns):
   #   Column                       Non-Null Count  Dtype
  ---  ------                       --------------  -----
   0   ProdTaken                    4888 non-null   int64
   1   Age                          4888 non-null   float64
   2   CityTier                     4888 non-null   int64
   3   DurationOfPitch              4888 non-null   float64
   4   NumberOfPersonVisiting       4888 non-null   int64
   5   NumberOfFollowups            4888 non-null   float64
   6   PreferredPropertyStar        4888 non-null   float64
   7   NumberOfTrips                4888 non-null   float64
   8   Passport                     4888 non-null   int64
   9   PitchSatisfactionScore       4888 non-null   int64
   10  OwnCar                       4888 non-null   int64
   11  NumberOfChildrenVisiting     4888 non-null   float64
   12  MonthlyIncome                4888 non-null   float64
   13  Gender_Male                  4888 non-null   uint8
   14  Occupation_Free Lancer       4888 non-null   uint8
   15  Occupation_Large Business    4888 non-null   uint8
   16  Occupation_Salaried          4888 non-null   uint8
   17  Occupation_Small Business    4888 non-null   uint8
   18  TypeofContact_Self Enquiry   4888 non-null   uint8
   19  MaritalStatus_Divorced       4888 non-null   uint8
   20  MaritalStatus_Married        4888 non-null   uint8
   21  MaritalStatus_Single         4888 non-null   uint8
   22  MaritalStatus_Unmarried      4888 non-null   uint8
   23  Designation_AVP              4888 non-null   uint8
   24  Designation_Executive        4888 non-null   uint8
   25  Designation_Manager          4888 non-null   uint8
   26  Designation_Senior Manager   4888 non-null   uint8
   27  Designation_VP               4888 non-null   uint8
   28  ProductPitched_Basic         4888 non-null   uint8
   29  ProductPitched_Deluxe        4888 non-null   uint8
   30  ProductPitched_King          4888 non-null   uint8
   31  ProductPitched_Standard      4888 non-null   uint8
   32  ProductPitched_Super Deluxe  4888 non-null   uint8
  dtypes: float64(7), int64(6), uint8(20)
  memory usage: 592.0 KB


  # Separating features and the target column
  X = vu_Tourism_df.drop('ProdTaken', axis=1)
  y = vu_Tourism_df['ProdTaken']


  # Splitting the data into train and test sets in 70:30 ratio
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=1, shuffle=True)


  X_train.shape, X_test.shape

((3421, 32), (1467, 32))


  dtc_estimator = DecisionTreeClassifier(random_state=1)
  dtc_estimator.fit(X_train,y_train)

DecisionTreeClassifier(random_state=1)

DecisionTreeClassifier(random_state=1)


  dtc_estimator_score=get_metrics_score(dtc_estimator)

Accuracy on training set :  1.0
  Accuracy on test set :  0.9161554192229039
  Recall on training set :  1.0
  Recall on test set :  0.7601351351351351
  Precision on training set :  1.0
  Precision on test set :  0.8122743682310469


  make_confusion_matrix(dtc_estimator,y_test)


  #base_estimator for bagging classifier is a decision tree by default
  bagging_estimator=BaggingClassifier(random_state=1)
  bagging_estimator.fit(X_train,y_train)

BaggingClassifier(random_state=1)

BaggingClassifier(random_state=1)


  #Using above defined function to get accuracy, recall and precision on train and test set
  bagging_estimator_score=get_metrics_score(bagging_estimator)

Accuracy on training set :  0.9944460684010523
  Accuracy on test set :  0.9079754601226994
  Recall on training set :  0.9711538461538461
  Recall on test set :  0.6148648648648649
  Precision on training set :  0.9983525535420099
  Precision on test set :  0.896551724137931


  make_confusion_matrix(bagging_estimator,y_test)


  #Train the random forest classifier
  rf_estimator=RandomForestClassifier(random_state=1)
  rf_estimator.fit(X_train,y_train)

RandomForestClassifier(random_state=1)

RandomForestClassifier(random_state=1)


  #Using above defined function to get accuracy, recall and precision on train and test set
  rf_estimator_score=get_metrics_score(rf_estimator)

Accuracy on training set :  1.0
  Accuracy on test set :  0.896387184730743
  Recall on training set :  1.0
  Recall on test set :  0.5236486486486487
  Precision on training set :  1.0
  Precision on test set :  0.9337349397590361


  make_confusion_matrix(rf_estimator,y_test)


  labels_metrics = np.array(['Accuracy Train','Accuracy Test','Recall Train','Recall Test','Precision Train','Precision Test'])
  label_ensemble_model = np.array(['Bagging','DecisionTreeClassifier','ForestTreeClassifier'])


  BDTRF_model_metrics =np.array([bagging_estimator_score,dtc_estimator_score,rf_estimator_score])


  BDTRF_metrics_df = pd.DataFrame(BDTRF_model_metrics, columns =[labels_metrics], index=[label_ensemble_model])
  BDTRF_metrics_df


  # Choose the type of classifier.
  bagging_estimator_tuned = BaggingClassifier(random_state=1)

  # Grid of parameters to choose from
  ## add from article
  parameters = {'max_samples': [0.7,0.8,0.9,1],
                'max_features': [0.7,0.8,0.9,1],
                'n_estimators' : [10,20,30,40,50],
               }

  # Type of scoring used to compare parameter combinations
  acc_scorer = metrics.make_scorer(metrics.recall_score)

  # Run the grid search
  grid_obj = GridSearchCV(bagging_estimator_tuned, parameters, scoring=acc_scorer,cv=5)
  grid_obj = grid_obj.fit(X_train, y_train)

  # Set the clf to the best combination of parameters
  bagging_estimator_tuned = grid_obj.best_estimator_

  # Fit the best algorithm to the data.
  bagging_estimator_tuned.fit(X_train, y_train)

BaggingClassifier(max_features=0.9, max_samples=0.9, n_estimators=50,
                    random_state=1)

BaggingClassifier(max_features=0.9, max_samples=0.9, n_estimators=50,
                    random_state=1)


  #Using above defined function to get accuracy, recall and precision on train and test set with Hyperparameters
  bagging_estimator_tuned_score=get_metrics_score(bagging_estimator_tuned)

Accuracy on training set :  0.9988307512423268
  Accuracy on test set :  0.918200408997955
  Recall on training set :  0.9935897435897436
  Recall on test set :  0.6114864864864865
  Precision on training set :  1.0
  Precision on test set :  0.9731182795698925


  make_confusion_matrix(bagging_estimator_tuned,y_test)


  label_ensemble_model3 = np.array(['Bagging','DecisionTreeClassifier','ForestTreeClassifier','Bagging_estimator_tuned'])
  BDTRF_model3_metrics =np.array([bagging_estimator_score,dtc_estimator_score,rf_estimator_score,bagging_estimator_tuned_score])
  BDTRF_metrics_df3 = pd.DataFrame(BDTRF_model3_metrics, columns =[labels_metrics], index=[label_ensemble_model3])
  BDTRF_metrics_df3


  bagging_lr=BaggingClassifier(base_estimator=LogisticRegression(solver='liblinear',random_state=1,max_iter=1000),random_state=1)
  bagging_lr.fit(X_train,y_train)

BaggingClassifier(base_estimator=LogisticRegression(max_iter=1000,
                                                      random_state=1,
                                                      solver='liblinear'),
                    random_state=1)

BaggingClassifier(base_estimator=LogisticRegression(max_iter=1000,
                                                      random_state=1,
                                                      solver='liblinear'),
                    random_state=1)

LogisticRegression(max_iter=1000, random_state=1, solver='liblinear')

LogisticRegression(max_iter=1000, random_state=1, solver='liblinear')


  #Using above defined function to get accuracy, recall and precision on train and test set
  bagging_lr_score=get_metrics_score(bagging_lr)

Accuracy on training set :  0.8254896229172757
  Accuracy on test set :  0.8070892978868439
  Recall on training set :  0.052884615384615384
  Recall on test set :  0.0472972972972973
  Precision on training set :  0.8461538461538461
  Precision on test set :  0.9333333333333333


  make_confusion_matrix(bagging_lr,y_test)


  label_ensemble_model4 = np.array(['Bagging','DecisionTreeClassifier','ForestTreeClassifier','Bagging_estimator_tuned','Bagging_LogisticReg'])
  BDTRF_model4_metrics =np.array([bagging_estimator_score,dtc_estimator_score,rf_estimator_score,bagging_estimator_tuned_score,bagging_lr_score])
  BDTRF_metrics_df4 = pd.DataFrame(BDTRF_model4_metrics, columns =[labels_metrics], index=[label_ensemble_model4])
  BDTRF_metrics_df4


  # Choose the type of classifier.
  rf_estimator_tuned = RandomForestClassifier(random_state=1)

  # Grid of parameters to choose from
  ## add from article
  parameters = {"n_estimators": [150,200,250],
      "min_samples_leaf": np.arange(5, 10),
      "max_features": np.arange(0.2, 0.7, 0.1),
      "max_samples": np.arange(0.3, 0.7, 0.1),
               }

  # Type of scoring used to compare parameter combinations
  acc_scorer = metrics.make_scorer(metrics.recall_score)

  # Run the grid search
  grid_obj = GridSearchCV(rf_estimator_tuned, parameters, scoring=acc_scorer,cv=5)
  grid_obj = grid_obj.fit(X_train, y_train)

  # Set the clf to the best combination of parameters
  rf_estimator_tuned = grid_obj.best_estimator_

  # Fit the best algorithm to the data.
  rf_estimator_tuned.fit(X_train, y_train)

RandomForestClassifier(max_features=0.6000000000000001,
                         max_samples=0.6000000000000001, min_samples_leaf=5,
                         n_estimators=150, random_state=1)

RandomForestClassifier(max_features=0.6000000000000001,
                         max_samples=0.6000000000000001, min_samples_leaf=5,
                         n_estimators=150, random_state=1)


  #Using above defined function to get accuracy, recall and precision on train and test set
  rf_estimator_tuned_score=get_metrics_score(rf_estimator_tuned)

Accuracy on training set :  0.9125986553639287
  Accuracy on test set :  0.8534423994546694
  Recall on training set :  0.5544871794871795
  Recall on test set :  0.3547297297297297
  Precision on training set :  0.9427792915531336
  Precision on test set :  0.813953488372093


  make_confusion_matrix(rf_estimator_tuned,y_test)


  label_ensemble_model5 = np.array(['Bagging','DecisionTreeClassifier','ForestTreeClassifier','Bagging_estimator_tuned','Bagging_LogisticReg','ForestTree_Tuned'])
  BDTRF_model5_metrics = np.array([bagging_estimator_score,dtc_estimator_score,rf_estimator_score,bagging_estimator_tuned_score,bagging_lr_score,rf_estimator_tuned_score])
  BDTRF_metrics_df5 = pd.DataFrame(BDTRF_model5_metrics, columns =[labels_metrics], index=[label_ensemble_model5])
  BDTRF_metrics_df5


  # Choose the type of classifier.
  rf_estimator_weighted = RandomForestClassifier(random_state=1)

  # Grid of parameters to choose from
  ## add from article
  parameters = {
      "class_weight": [{0: 0.3, 1: 0.7}],
      "n_estimators": [100,150,200,250],
      "min_samples_leaf": np.arange(5, 10),
      "max_features": np.arange(0.2, 0.7, 0.1),
      "max_samples": np.arange(0.3, 0.7, 0.1),
  }

  # Type of scoring used to compare parameter combinations
  acc_scorer = metrics.make_scorer(metrics.recall_score)

  # Run the grid search
  grid_obj = GridSearchCV(rf_estimator_weighted, parameters, scoring=acc_scorer,cv=5)
  grid_obj = grid_obj.fit(X_train, y_train)

  # Set the clf to the best combination of parameters
  rf_estimator_weighted = grid_obj.best_estimator_

  # Fit the best algorithm to the data.
  rf_estimator_weighted.fit(X_train, y_train)

RandomForestClassifier(class_weight={0: 0.3, 1: 0.7},
                         max_features=0.6000000000000001,
                         max_samples=0.6000000000000001, min_samples_leaf=6,
                         random_state=1)

RandomForestClassifier(class_weight={0: 0.3, 1: 0.7},
                         max_features=0.6000000000000001,
                         max_samples=0.6000000000000001, min_samples_leaf=6,
                         random_state=1)


  #Using above defined function to get accuracy, recall and precision on train and test set
  rf_estimator_weighted_score=get_metrics_score(rf_estimator_weighted)

Accuracy on training set :  0.9196141479099679
  Accuracy on test set :  0.8629856850715747
  Recall on training set :  0.7387820512820513
  Recall on test set :  0.5101351351351351
  Precision on training set :  0.8045375218150087
  Precision on test set :  0.7294685990338164


  make_confusion_matrix(rf_estimator_weighted,y_test)


  label_ensemble_model6 = np.array(['Bagging','DecisionTreeClassifier','ForestTreeClassifier','Bagging_estimator_tuned','Bagging_LogisticReg','ForestTree_Tuned','ForestTree_Weighted'])
  BDTRF_model6_metrics =np.array([bagging_estimator_score,dtc_estimator_score,rf_estimator_score,bagging_estimator_tuned_score,bagging_lr_score,rf_estimator_tuned_score,rf_estimator_weighted_score])
  BDTRF_metrics_df6 = pd.DataFrame(BDTRF_model6_metrics, columns =[labels_metrics], index=[label_ensemble_model6])
  BDTRF_metrics_df6


  importances = rf_estimator_weighted.feature_importances_
  indices = np.argsort(importances)
  feature_names = list(X.columns)

  plt.figure(figsize=(12,12))
  plt.title('Feature Importances')
  plt.barh(range(len(indices)), importances[indices], color='violet', align='center')
  plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
  plt.xlabel('Relative Importance')
  plt.show()


  abc = AdaBoostClassifier(random_state=1)
  abc.fit(X_train,y_train)

AdaBoostClassifier(random_state=1)

AdaBoostClassifier(random_state=1)


  #Using above defined function to get accuracy, recall and precision on train and test set
  abc_score=get_metrics_score(abc)

Accuracy on training set :  0.8494592224495762
  Accuracy on test set :  0.830947511929107
  Recall on training set :  0.3301282051282051
  Recall on test set :  0.27702702702702703
  Precision on training set :  0.6798679867986799
  Precision on test set :  0.7068965517241379


  make_confusion_matrix(abc,y_test)


  gbc = GradientBoostingClassifier(random_state=1)
  gbc.fit(X_train,y_train)

GradientBoostingClassifier(random_state=1)

GradientBoostingClassifier(random_state=1)


  #Using above defined function to get accuracy, recall and precision on train and test set
  gbc_score=get_metrics_score(gbc)

Accuracy on training set :  0.8892136802104648
  Accuracy on test set :  0.8486707566462167
  Recall on training set :  0.46955128205128205
  Recall on test set :  0.3277027027027027
  Precision on training set :  0.8592375366568915
  Precision on test set :  0.8083333333333333


  make_confusion_matrix(gbc,y_test)


  xgb = XGBClassifier(random_state=1,eval_metric='logloss')
  xgb.fit(X_train, y_train)

XGBClassifier(base_score=None, booster=None, callbacks=None,
                colsample_bylevel=None, colsample_bynode=None,
                colsample_bytree=None, device=None, early_stopping_rounds=None,
                enable_categorical=False, eval_metric='logloss',
                feature_types=None, gamma=None, grow_policy=None,
                importance_type=None, interaction_constraints=None,
                learning_rate=None, max_bin=None, max_cat_threshold=None,
                max_cat_to_onehot=None, max_delta_step=None, max_depth=None,
                max_leaves=None, min_child_weight=None, missing=nan,
                monotone_constraints=None, multi_strategy=None, n_estimators=None,
                n_jobs=None, num_parallel_tree=None, random_state=1, ...)

XGBClassifier(base_score=None, booster=None, callbacks=None,
                colsample_bylevel=None, colsample_bynode=None,
                colsample_bytree=None, device=None, early_stopping_rounds=None,
                enable_categorical=False, eval_metric='logloss',
                feature_types=None, gamma=None, grow_policy=None,
                importance_type=None, interaction_constraints=None,
                learning_rate=None, max_bin=None, max_cat_threshold=None,
                max_cat_to_onehot=None, max_delta_step=None, max_depth=None,
                max_leaves=None, min_child_weight=None, missing=nan,
                monotone_constraints=None, multi_strategy=None, n_estimators=None,
                n_jobs=None, num_parallel_tree=None, random_state=1, ...)


  #Using above defined function to get accuracy, recall and precision on train and test set
  xgb_score=get_metrics_score(xgb)

Accuracy on training set :  0.9997076878105817
  Accuracy on test set :  0.9222903885480572
  Recall on training set :  0.9983974358974359
  Recall on test set :  0.668918918918919
  Precision on training set :  1.0
  Precision on test set :  0.9252336448598131


  make_confusion_matrix(xgb,y_test)


  # Choose the type of classifier.
  abc_tuned = AdaBoostClassifier(random_state=1)

  # Grid of parameters to choose from
  ## add from article
  parameters = {
      #Let's try different max_depth for base_estimator
      "base_estimator":[DecisionTreeClassifier(max_depth=1, random_state=1),DecisionTreeClassifier(max_depth=2, random_state=1),DecisionTreeClassifier(max_depth=3, random_state=1)],
      "n_estimators": np.arange(10,110,10),
      "learning_rate":np.arange(0.1,2,0.1)
  }

  # Type of scoring used to compare parameter combinations
  acc_scorer = metrics.make_scorer(metrics.recall_score)

  # Run the grid search
  grid_obj = GridSearchCV(abc_tuned, parameters, scoring=acc_scorer,cv=5)
  grid_obj = grid_obj.fit(X_train, y_train)

  # Set the clf to the best combination of parameters
  abc_tuned = grid_obj.best_estimator_

  # Fit the best algorithm to the data.
  abc_tuned.fit(X_train, y_train)

AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=3,
                                                           random_state=1),
                     learning_rate=1.1, n_estimators=100, random_state=1)

AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=3,
                                                           random_state=1),
                     learning_rate=1.1, n_estimators=100, random_state=1)

DecisionTreeClassifier(max_depth=3, random_state=1)

DecisionTreeClassifier(max_depth=3, random_state=1)


  #Using above defined function to get accuracy, recall and precision on train and test set
  abc_tuned_score=get_metrics_score(abc_tuned)

Accuracy on training set :  0.9891844489915229
  Accuracy on test set :  0.89093387866394
  Recall on training set :  0.9567307692307693
  Recall on test set :  0.6148648648648649
  Precision on training set :  0.9835255354200988
  Precision on test set :  0.7982456140350878


  make_confusion_matrix(abc_tuned,y_test)


  importances = abc_tuned.feature_importances_
  indices = np.argsort(importances)
  feature_names = list(X.columns)

  plt.figure(figsize=(12,12))
  plt.title('Feature Importances')
  plt.barh(range(len(indices)), importances[indices], color='violet', align='center')
  plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
  plt.xlabel('Relative Importance')
  plt.show()


  gbc_init = GradientBoostingClassifier(init=AdaBoostClassifier(random_state=1),random_state=1)
  gbc_init.fit(X_train,y_train)

GradientBoostingClassifier(init=AdaBoostClassifier(random_state=1),
                             random_state=1)

GradientBoostingClassifier(init=AdaBoostClassifier(random_state=1),
                             random_state=1)

AdaBoostClassifier(random_state=1)

AdaBoostClassifier(random_state=1)


  #Using above defined function to get accuracy, recall and precision on train and test set
  gbc_init_score=get_metrics_score(gbc_init)

Accuracy on training set :  0.8871674948845367
  Accuracy on test set :  0.8534423994546694
  Recall on training set :  0.46314102564102566
  Recall on test set :  0.3547297297297297
  Precision on training set :  0.85
  Precision on test set :  0.813953488372093


  # Choose the type of classifier.
  gbc_tuned = GradientBoostingClassifier(init=AdaBoostClassifier(random_state=1),random_state=1)

  # Grid of parameters to choose from
  ## add from article
  parameters = {
      "n_estimators": [100,150,200,250],
      "subsample":[0.8,0.9,1],
      "max_features":[0.7,0.8,0.9,1]
  }

  # Type of scoring used to compare parameter combinations
  acc_scorer = metrics.make_scorer(metrics.recall_score)

  # Run the grid search
  grid_obj = GridSearchCV(gbc_tuned, parameters, scoring=acc_scorer,cv=5)
  grid_obj = grid_obj.fit(X_train, y_train)

  # Set the clf to the best combination of parameters
  gbc_tuned = grid_obj.best_estimator_

  # Fit the best algorithm to the data.
  gbc_tuned.fit(X_train, y_train)

GradientBoostingClassifier(init=AdaBoostClassifier(random_state=1),
                             max_features=0.9, n_estimators=250, random_state=1,
                             subsample=0.9)

GradientBoostingClassifier(init=AdaBoostClassifier(random_state=1),
                             max_features=0.9, n_estimators=250, random_state=1,
                             subsample=0.9)

AdaBoostClassifier(random_state=1)

AdaBoostClassifier(random_state=1)


  #Using above defined function to get accuracy, recall and precision on train and test set
  gbc_tuned_score=get_metrics_score(gbc_tuned)

Accuracy on training set :  0.9286758257819351
  Accuracy on test set :  0.880027266530334
  Recall on training set :  0.6538461538461539
  Recall on test set :  0.47297297297297297
  Precision on training set :  0.9357798165137615
  Precision on test set :  0.875


  make_confusion_matrix(gbc_tuned,y_test)


  importances = gbc_tuned.feature_importances_
  indices = np.argsort(importances)
  feature_names = list(X.columns)

  plt.figure(figsize=(12,12))
  plt.title('Feature Importances')
  plt.barh(range(len(indices)), importances[indices], color='violet', align='center')
  plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
  plt.xlabel('Relative Importance')
  plt.show()


  # Choose the type of classifier.
  xgb_tuned = XGBClassifier(random_state=1,eval_metric='logloss')

  # Grid of parameters to choose from
  ## add from
  parameters = {
      "n_estimators": np.arange(10,100,20),
      "scale_pos_weight":[0,1,2,5],
      "subsample":[0.5,0.7,0.9,1],
      "learning_rate":[0.01,0.1,0.2,0.05],
      "gamma":[0,1,3],
      "colsample_bytree":[0.5,0.7,0.9,1],
      "colsample_bylevel":[0.5,0.7,0.9,1]
  }

  # Type of scoring used to compare parameter combinations
  acc_scorer = metrics.make_scorer(metrics.recall_score)

  # Run the grid search
  grid_obj = GridSearchCV(xgb_tuned, parameters,scoring=acc_scorer,cv=5)
  grid_obj = grid_obj.fit(X_train, y_train)

  # Set the clf to the best combination of parameters
  xgb_tuned = grid_obj.best_estimator_

  # Fit the best algorithm to the data.
  xgb_tuned.fit(X_train, y_train)

XGBClassifier(base_score=None, booster=None, callbacks=None,
                colsample_bylevel=0.5, colsample_bynode=None,
                colsample_bytree=0.5, device=None, early_stopping_rounds=None,
                enable_categorical=False, eval_metric='logloss',
                feature_types=None, gamma=3, grow_policy=None,
                importance_type=None, interaction_constraints=None,
                learning_rate=0.01, max_bin=None, max_cat_threshold=None,
                max_cat_to_onehot=None, max_delta_step=None, max_depth=None,
                max_leaves=None, min_child_weight=None, missing=nan,
                monotone_constraints=None, multi_strategy=None, n_estimators=10,
                n_jobs=None, num_parallel_tree=None, random_state=1, ...)

XGBClassifier(base_score=None, booster=None, callbacks=None,
                colsample_bylevel=0.5, colsample_bynode=None,
                colsample_bytree=0.5, device=None, early_stopping_rounds=None,
                enable_categorical=False, eval_metric='logloss',
                feature_types=None, gamma=3, grow_policy=None,
                importance_type=None, interaction_constraints=None,
                learning_rate=0.01, max_bin=None, max_cat_threshold=None,
                max_cat_to_onehot=None, max_delta_step=None, max_depth=None,
                max_leaves=None, min_child_weight=None, missing=nan,
                monotone_constraints=None, multi_strategy=None, n_estimators=10,
                n_jobs=None, num_parallel_tree=None, random_state=1, ...)


  #Using above defined function to get accuracy, recall and precision on train and test set
  xgb_tuned_score=get_metrics_score(xgb_tuned)

Accuracy on training set :  0.3095586085939784
  Accuracy on test set :  0.3094751192910702
  Recall on training set :  0.9983974358974359
  Recall on test set :  0.9864864864864865
  Precision on training set :  0.20878016085790885
  Precision on test set :  0.22444273635664874


  make_confusion_matrix(xgb_tuned,y_test)


  importances = xgb_tuned.feature_importances_
  indices = np.argsort(importances)
  feature_names = list(X.columns)

  plt.figure(figsize=(12,12))
  plt.title('Feature Importances')
  plt.barh(range(len(indices)), importances[indices], color='violet', align='center')
  plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
  plt.xlabel('Relative Importance')
  plt.show()


  # defining list of models
  models = [abc, abc_tuned, gbc, gbc_init, gbc_tuned, xgb, xgb_tuned]

  # defining empty lists to add train and test results
  acc_train = []
  acc_test = []
  recall_train = []
  recall_test = []
  precision_train = []
  precision_test = []

  # looping through all the models to get the accuracy, precall and precision scores
  for model in models:
      j = get_metrics_score(model,False)
      acc_train.append(np.round(j[0],2))
      acc_test.append(np.round(j[1],2))
      recall_train.append(np.round(j[2],2))
      recall_test.append(np.round(j[3],2))
      precision_train.append(np.round(j[4],2))
      precision_test.append(np.round(j[5],2))


  comparison_frame = pd.DataFrame({'Model':['AdaBoost with default paramters','AdaBoost Tuned',
                                            'Gradient Boosting with default parameters','Gradient Boosting with init=AdaBoost',
                                            'Gradient Boosting Tuned','XGBoost with default parameters','XGBoost Tuned'],
                                            'Train_Accuracy': acc_train,'Test_Accuracy': acc_test,
                                            'Train_Recall':recall_train,'Test_Recall':recall_test,
                                            'Train_Precision':precision_train,'Test_Precision':precision_test})
  comparison_frame

	Unnamed: 0	Unnamed: 1	Unnamed: 2	Unnamed: 3
0	NaN	Data	Variable	Discerption
1	NaN	Tourism	CustomerID	Unique customer ID
2	NaN	Tourism	ProdTaken	Whether the customer has purchased a package o...
3	NaN	Tourism	Age	Age of customer
4	NaN	Tourism	TypeofContact	How customer was contacted (Company Invited or...
5	NaN	Tourism	CityTier	City tier depends on the development of a city...
6	NaN	Tourism	DurationOfPitch	Duration of the pitch by a salesperson to the ...
7	NaN	Tourism	Occupation	Occupation of customer
8	NaN	Tourism	Gender	Gender of customer
9	NaN	Tourism	NumberOfPersonVisiting	Total number of persons planning to take the t...
10	NaN	Tourism	NumberOfFollowups	Total number of follow-ups has been done by sa...
11	NaN	Tourism	ProductPitched	Product pitched by the salesperson
12	NaN	Tourism	PreferredPropertyStar	Preferred hotel property rating by customer
13	NaN	Tourism	MaritalStatus	Marital status of customer
14	NaN	Tourism	NumberOfTrips	Average number of trips in a year by customer
15	NaN	Tourism	Passport	The customer has a passport or not (0: No, 1: ...
16	NaN	Tourism	PitchSatisfactionScore	Sales pitch satisfaction score
17	NaN	Tourism	OwnCar	Whether the customers own a car or not (0: No,...
18	NaN	Tourism	NumberOfChildrenVisiting	Total number of children with age less than 5 ...
19	NaN	Tourism	Designation	Designation of the customer in the current org...
20	NaN	Tourism	MonthlyIncome	Gross monthly income of the customer

	CustomerID	ProdTaken	Age	TypeofContact	CityTier	DurationOfPitch	Occupation	Gender	NumberOfPersonVisiting	NumberOfFollowups	ProductPitched	PreferredPropertyStar	MaritalStatus	NumberOfTrips	Passport	PitchSatisfactionScore	OwnCar	NumberOfChildrenVisiting	Designation	MonthlyIncome
0	200000	1	41.0	Self Enquiry	3	6.0	Salaried	Female	3	3.0	Deluxe	3.0	Single	1.0	1	2	1	0.0	Manager	20993.0
1	200001	0	49.0	Company Invited	1	14.0	Salaried	Male	3	4.0	Deluxe	4.0	Divorced	2.0	0	3	1	2.0	Manager	20130.0
2	200002	1	37.0	Self Enquiry	1	8.0	Free Lancer	Male	3	4.0	Basic	3.0	Single	7.0	1	3	0	0.0	Executive	17090.0
3	200003	0	33.0	Company Invited	1	9.0	Salaried	Female	2	3.0	Basic	3.0	Divorced	2.0	1	5	1	1.0	Executive	17909.0
4	200004	0	NaN	Self Enquiry	1	8.0	Small Business	Male	2	3.0	Basic	4.0	Divorced	1.0	0	5	1	0.0	Executive	18468.0
5	200005	0	32.0	Company Invited	1	8.0	Salaried	Male	3	3.0	Basic	3.0	Single	1.0	0	5	1	1.0	Executive	18068.0
6	200006	0	59.0	Self Enquiry	1	9.0	Small Business	Female	2	2.0	Basic	5.0	Divorced	5.0	1	2	1	1.0	Executive	17670.0
7	200007	0	30.0	Self Enquiry	1	30.0	Salaried	Male	3	3.0	Basic	3.0	Married	2.0	0	2	0	1.0	Executive	17693.0
8	200008	0	38.0	Company Invited	1	29.0	Salaried	Male	2	4.0	Standard	3.0	Unmarried	1.0	0	3	0	0.0	Senior Manager	24526.0
9	200009	0	36.0	Self Enquiry	1	33.0	Small Business	Male	3	3.0	Deluxe	3.0	Divorced	7.0	0	3	1	0.0	Manager	20237.0
10	200010	0	35.0	Self Enquiry	1	22.0	Small Business	Male	2	2.0	Basic	4.0	Divorced	1.0	0	3	1	1.0	Executive	17426.0
11	200011	0	NaN	Self Enquiry	1	21.0	Salaried	Female	2	4.0	Deluxe	3.0	Single	1.0	1	3	0	0.0	Manager	NaN
12	200012	0	31.0	Self Enquiry	1	32.0	Salaried	Male	2	3.0	Basic	3.0	Married	2.0	0	3	0	1.0	Executive	17911.0
13	200013	0	34.0	Self Enquiry	1	25.0	Small Business	Male	3	3.0	Basic	3.0	Married	1.0	0	3	0	2.0	Executive	17661.0
14	200014	1	28.0	Self Enquiry	1	30.0	Salaried	Male	2	4.0	Basic	3.0	Single	6.0	1	2	0	0.0	Executive	17028.0
15	200015	0	29.0	Self Enquiry	1	27.0	Salaried	Female	2	2.0	Standard	5.0	Married	2.0	0	5	1	1.0	Senior Manager	24980.0
16	200016	0	32.0	Self Enquiry	1	11.0	Salaried	Male	3	2.0	Basic	4.0	Married	1.0	1	2	1	0.0	Executive	18298.0
17	200017	0	22.0	Company Invited	1	22.0	Small Business	Male	3	2.0	Basic	3.0	Married	2.0	1	3	0	0.0	Executive	17935.0
18	200018	0	53.0	Self Enquiry	3	8.0	Salaried	Female	3	4.0	Super Deluxe	3.0	Divorced	3.0	0	3	1	0.0	AVP	30427.0
19	200019	0	NaN	Self Enquiry	1	8.0	Salaried	Male	2	3.0	Basic	3.0	Single	6.0	1	4	0	1.0	Executive	NaN

	count	mean	std	min	25%	50%	75%	max
ProdTaken	4888.0	0.188216	0.390925	0.0	0.0	0.0	0.0	1.0
Age	4662.0	37.622265	9.316387	18.0	31.0	36.0	44.0	61.0
CityTier	4888.0	1.654255	0.916583	1.0	1.0	1.0	3.0	3.0
DurationOfPitch	4637.0	15.490835	8.519643	5.0	9.0	13.0	20.0	127.0
NumberOfPersonVisiting	4888.0	2.905074	0.724891	1.0	2.0	3.0	3.0	5.0
NumberOfFollowups	4843.0	3.708445	1.002509	1.0	3.0	4.0	4.0	6.0
PreferredPropertyStar	4862.0	3.581037	0.798009	3.0	3.0	3.0	4.0	5.0
NumberOfTrips	4748.0	3.236521	1.849019	1.0	2.0	3.0	4.0	22.0
Passport	4888.0	0.290917	0.454232	0.0	0.0	0.0	1.0	1.0
PitchSatisfactionScore	4888.0	3.078151	1.365792	1.0	2.0	3.0	4.0	5.0
OwnCar	4888.0	0.620295	0.485363	0.0	0.0	1.0	1.0	1.0
NumberOfChildrenVisiting	4822.0	1.187267	0.857861	0.0	1.0	1.0	2.0	3.0
MonthlyIncome	4655.0	23619.853491	5380.698361	1000.0	20346.0	22347.0	25571.0	98678.0

	ProdTaken	Age	TypeofContact	CityTier	DurationOfPitch	Occupation	Gender	NumberOfPersonVisiting	NumberOfFollowups	ProductPitched	PreferredPropertyStar	MaritalStatus	NumberOfTrips	Passport	PitchSatisfactionScore	OwnCar	NumberOfChildrenVisiting	Designation	MonthlyIncome
1434	0	37.0	Company Invited	3	126.0	Salaried	Male	2	3.0	Basic	3.0	Married	3.0	0	1	1	1.0	Executive	18482.0
3878	0	53.0	Company Invited	3	127.0	Salaried	Male	3	4.0	Basic	3.0	Married	4.0	0	1	1	2.0	Executive	22160.0

	Age	TypeofContact	CityTier	DurationOfPitch	Occupation	Gender	NumberOfPersonVisiting	NumberOfFollowups	ProductPitched	PreferredPropertyStar	MaritalStatus	NumberOfTrips	Passport	PitchSatisfactionScore	OwnCar	NumberOfChildrenVisiting	Designation	MonthlyIncome
326	39.0	Company Invited	1	5.0	Small Business	Male	2	4.0	King	3.0	Divorced	2.0	0	5	1	1.0	VP	34272.0
1126	50.0	Self Enquiry	3	5.0	Small Business	Male	2	3.0	King	3.0	Married	5.0	1	5	0	1.0	VP	34331.0
1526	35.0	Company Invited	3	5.0	Salaried	Male	3	3.0	Standard	5.0	Married	2.0	0	4	0	0.0	Senior Manager	24069.0
1901	37.0	Self Enquiry	1	5.0	Salaried	Female	2	3.0	Deluxe	3.0	Single	1.0	0	1	0	0.0	Manager	NaN
2276	52.0	Self Enquiry	1	5.0	Salaried	Male	2	3.0	Standard	4.0	Unmarried	1.0	0	3	1	1.0	Senior Manager	25445.0
2326	20.0	Self Enquiry	1	5.0	Salaried	Male	2	4.0	Basic	3.0	Single	2.0	0	3	0	0.0	Executive	18033.0

	Accuracy Train	Accuracy Test	Recall Train	Recall Test	Precision Train	Precision Test
Bagging	0.994446	0.907975	0.971154	0.614865	0.998353	0.896552
DecisionTreeClassifier	1.000000	0.916155	1.000000	0.760135	1.000000	0.812274
ForestTreeClassifier	1.000000	0.896387	1.000000	0.523649	1.000000	0.933735

	Model	Train_Accuracy	Test_Accuracy	Train_Recall	Test_Recall	Train_Precision	Test_Precision
0	AdaBoost with default paramters	0.85	0.83	0.33	0.28	0.68	0.71
1	AdaBoost Tuned	0.99	0.89	0.96	0.61	0.98	0.80
2	Gradient Boosting with default parameters	0.89	0.85	0.47	0.33	0.86	0.81
3	Gradient Boosting with init=AdaBoost	0.89	0.85	0.46	0.35	0.85	0.81
4	Gradient Boosting Tuned	0.93	0.88	0.65	0.47	0.94	0.88
5	XGBoost with default parameters	1.00	0.92	1.00	0.67	1.00	0.93
6	XGBoost Tuned	0.31	0.31	1.00	0.99	0.21	0.22

Predicting Tour Package Customers for Fantasy Tours¶

Objective¶

Data Dictionary¶

Setup¶

Import Libraries¶

Useful Functions¶

Setup¶

Initial Data Exploration¶

EDA and Feature Engineering¶

Overview of Dataset¶

Univariate Analysis¶

Feature ProdTaken¶

Feature Age¶

Feature Gender¶

Feature TypeofContact¶

Feature DurationOfPitch¶

Feature NumberOfFollowups¶

Feature Occupation¶

Feature PreferredPropertyStar¶

Feature NumberOfTrips¶

Feature NumberOfChildrenVisiting¶

Feature MaritalStatus¶

Feature Passport¶

Feature PitchSatisfactionScore¶

Feature OwnCar¶

Feature City Tier¶

Feature MonthlyIncome¶

Feature Designation¶

ProductPitched¶

Bivariate Analysis¶

Correlation Check¶

Contuining Imputation of Null Values¶

NumberOfTrips¶

Outliers¶

Bivariate Plots¶

Categorical Features to Numerical Features¶

Modeling¶

Data Preparation for model Building¶

Decision Tree Classifier¶

Bagging Classifier¶

Random Forest Classifier¶

Comparison of Models¶

Hyperparameter Tuning¶

Feature Importance¶

Boosting¶

AdaBoost Classifier¶

Gradient Boosting Classifier¶

XGBoost Classifier¶

Hyperparameter Tuning¶

Adaboot Classifier¶

Comparing Models¶

Insights¶

Recommendations¶